Thermal proteome profiling of stationary-phase E. coli treated with semapimod
Ontology highlight
ABSTRACT: There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally novel compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.
INSTRUMENT(S): Orbitrap Exploris 480
ORGANISM(S): Escherichia Coli
SUBMITTER: Alice Herneisen
LAB HEAD: Sebastian Lourido
PROVIDER: PXD044230 | Pride | 2023-08-21
REPOSITORIES: Pride
ACCESS DATA